MoDNN: Local distributed mobile computing system for Deep Neural Network

By Jiachen Mao, Xiang Chen, Kent W. Nixon, Christopher D. Krieger, Yiran Chen. Design, Automation and Test in Europe, 2017

296 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d246989d6839a91f38b7fa97d1ee67545d636db6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Khaled Ben Letaief
2. Xu Chen
3. Zhi Zhou
4. Liekang Zeng
5. Junshan Zhang
6. Yuanming Shi
7. Xiaofei Wang
8. Yiwen Han

In Computer Vision and Pattern Recognition

1. Xu Chen
2. Khaled Ben Letaief
3. Liekang Zeng
4. Zhi Zhou
5. Bashir I. Morshed
6. Syed Kamrul Islam
7. Jianlin Cheng
8. Md Maruf Hossain Shuvo

Citations in the corpus

• SPINN: synergistic progressive inference of neural networks over device and cloud (2020, background): “Simultaneously, various works [50, 51, 85] have examined the case where the client device can offload to other devices in the local network.”
• Deep Learning With Edge Computing: A Review (2019, methodology): “[107] J. Mao, X. Chen, K. W. Nixon, C. Krieger, and Y. Chen, “MoDNN: Local distributed mobile computing system for deep neural network,” in Proc.”
• CoEdge: Cooperative DNN Inference With Adaptive Workload Partitioning Over Heterogeneous Edge Devices (2020, methodology): “On the one hand, the local approach does not incur communication costs, while MoDNN and Musical Chair need frequent cross-device communication during the runtime, which takes energy.”
• AccPar: Tensor Partitioning for Heterogeneous Deep Learning Accelerators (2020, unknown): “…DNN [69], Bit Fusion [70], Cn-vlutin [99], TIE [137], Laconic [138], ADM-NN [139], Gist [140] Co-Design DPS-CNN [71], Minerva [72], MoDNN [73], MeDNN [74], AdaLearner [75], Stitch-X [76], PIM-DNN [77], Scalpel [78], CirCNN [79], CoSMIC [80], SnaPEA [81], OLAcce [82], Prediction-based DNN…”
• Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review (2023, background): “MoDNN [371] is a horizontally distributed platform for DL inference in resource-constrained edge devices.”
• Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “This reduces the communications overhead, offloads data traffic, reduces user-side latency, and lightens the sever-side computational burdens [133], [134].”
• DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters (2018, methodology): “For a BODP- and MR-based execution model, an optimal approach is thereby to match the number of partitions to the number of available devices [16], which we will use for all further comparisons.”
• Communication-Efficient Edge AI: Algorithms and Systems (2020, methodology): “Different methods have been developed [207], [208] to partition a pre-trained DNN over several mobile devices in order to accelerate DNN inference on devices.”
• Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing (2019, methodology): “Note that DNN layers are partitioned horizontally in MoDNN and MeDNN, whereas DeepThings [86] employs a fused tile partitioning method that partitions the DNN layers vertically to reduce the memory footprint.”
• Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, methodology): “MoDNN, proposed in [177], executes DL in a local distributed mobile computing system”